RESUMO
Background: Hepatocellular carcinoma (HCC) is one of the most common cancers with high mortality in the world. HCC screening and diagnostic models are becoming effective strategies to reduce mortality and improve the overall survival (OS) of patients. Here, we expected to establish an effective novel diagnostic model based on new genes and explore potential drugs for HCC therapy. Methods: The gene expression data of HCC and normal samples (GSE14811, GSE60502, GSE84402, GSE101685, GSE102079, GSE113996, and GSE45436) were downloaded from the Gene Expression Omnibus (GEO) dataset. Bioinformatics analysis was performed to distinguish two differentially expressed genes (DEGs), diagnostic candidate genes, and functional enrichment pathways. QRT-PCR was used to validate the expression of diagnostic candidate genes. A diagnostic model based on candidate genes was established by an artificial neural network (ANN). Drug sensitivity analysis was used to explore potential drugs for HCC. CCK-8 assay was used to detect the viability of HepG2 under various presentative chemotherapy drugs. Results: There were 82 DEGs in cancer tissues compared to normal tissue. Protein-protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and infiltrating immune cell analysis were administered and analyzed. Diagnostic-related genes of MT1M, SPINK1, AKR1B10, and SLCO1B3 were selected from DEGs and used to construct a diagnostic model. The receiver operating characteristic (ROC) curves were 0.910 and 0.953 in the training and testing cohorts, respectively. Potential drugs, including vemurafenib, LOXO-101, dabrafenib, selumetinib, Arry-162, and NMS-E628, were found as well. Vemurafenib, dabrafenib, and selumetinib were observed to significantly affect HepG2 cell viability. Conclusion: The diagnostic model based on the four diagnostic-related genes by the ANN could provide predictive significance for diagnosis of HCC patients, which would be worthy of clinical application. Also, potential chemotherapy drugs might be effective for HCC therapy.
RESUMO
The study focused on the toxicological effect of Di-n-butyl phthalate (DBP) on the expression of Phosphorylated signal transducer and activator of transcription 1 (pSTAT1) -regulated Forkhead box protein M1 (FoxM1), which might provide a new understanding of gestational diabetes mellitus (GDM) development and a potential target for treatment. Streptozotocin (STZ) (40 mg/kg) was introduced in maternal rats by intraperitoneal injection on gestation day 0 (GD 0) in the STZ and STZ + DBP groups. DBP was introduced in maternal rats by oral feeding in the STZ + DBP group over the following 3 days (750 mg/kg/day). The changes in fasting blood glucose level in rats were detected on GD 1 and GD 5. The insulin levels in maternal rats and PIBCs were measured on GD 18. The Oral Glucose Tolerance Test (OGTT) test was performed on GD 18 to check the stability of the GDM model. The primary islet ß cells (PIBCs) were established for in vitro experiments. We examined the FoxM1 and pSTAT1 expression in pancreas by immunohistochemistry. Real-time PCR and Western blot were used to detect the pSTAR1 and FoxM1 protein and mRNA gene expression levels in PIBCs. Cell Counting Kit-8 (CCK-8) and flow cytometric analysis was used to test the viability and apoptosis of cells. The results showed that the STZ + DBP group had higher glucose and lower insulin secretion levels than the other groups by both fasting test and OGTT. FoxM1 was significantly suppressed while pSTAT1 was highly expressed after DBP exposure. FoxM1 could be regulated by pSTAT1. DBP can influence the progression of GDM through its toxicological effect, which significantly increases the expression of pSTAT1 and suppresses FoxM1, causing a decline in ß cell viability.
